多omics机器学习研究早期结直肠癌的宿主微生物群相互作用
Thejus T Jayakrishnan1,2, Naseer Sangwan3, Shimoli V Barot1
1Department of Hematology-Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA.
NPJ precision oncology
|July 17, 2024
概括
机器学习揭示了早期结直肠癌 (eoCRC) 中不同的宿主微生物组联系. 一个代谢学分类器显示出高准确度,表明eoCRC和治疗点的潜在生物标志物.
科学领域:
- 在瘤学瘤学.
- 微生物组研究 微生物组研究
- 代谢学 代谢学 代谢学
- 机器学习 机器学习
背景情况:
- 早期结直肠癌 (eoCRC) 发病率正在增加,其病因不明.
- 主体微生物群相互作用与CRC发展有关.
- 了解 eoCRC 中的独特协会对于有针对性的干预至关重要.
研究的目的:
- 使用机器学习研究eocrc与平均发作的crc (aocrc) 中不同的宿主微生物组关联.
- 通过整合微生物组和代谢组数据来确定eoCRC的潜在生物标志物.
- 根据确定的主体微生物组相关性,探索治疗干预的机会.
主要方法:
- 在CRC患者中对瘤组织微生物组 (16SrRNA测序) 和血代谢组进行配对分析 (n=64).
- 分类为eoCRC (年龄 ≤ 50) 和aoCRC (年龄 ≥ 60).
- 应用DIABLO机器学习用于多omics数据集成和分类器开发;差异关联网络分析.
主要成果:
- 多原子维度缩小揭示了eocrc和eocrc之间明显的集群模式.
- 代谢学分类器实现了高诊断性能 (AUC=0.98),超过了微生物组分类器 (AUC=0.61).
- 确定了特定的微生物代谢物相关性,包括甘油和伪尿氨酸与帕拉苏特雷拉/鲁米诺可可科科科,以及胆固醇/利醇与Erysipelatoclostridium/Eubacterium/Acidovorax.
结论:
- 多omics分析有效地揭示了eoCRC中的宿主微生物组相关性.
- 代谢学分类器显示出作为eoCRC的生物标志物具有显著的潜力.
- 独特的宿主微生物组相关性,特别是涉及eoCRC中的尿素循环,可能会呈现出新的治疗点.
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